(1) Technical Field
The present invention relates to the construction of decision probabilistic models, and their derivation, and more particularly to a tool for converting decision flowcharts into decision probabilistic models.
(2) Discussion
Most of the decision systems used in practice are based on decision flowcharts or databases of decision cases. These techniques have many shortcomings when compared with probabilistic graphical models, which are generally advocated for advanced decisions. They are much less accurate, less flexible, harder to maintain, and their complexity grows exponentially with the size of a diagnosed system. However, conventional decision flowcharts exist for many systems, and experts typically capture decision knowledge in the form of flowcharts.
Conventional decision flowcharts are very popular tools with severe decision limitations and high creation and modification costs. Despite the fact that decision flowcharts are the most common graphical form for expressing decision procedures, their design is time-consuming and requires extensive expertise in diagnosing a system for which it is intended. Such flowcharts consist of ordered observations (e.g., symptoms, error messages, and tests) and failures. The user follows a fixed sequence of observations and arrives at the root-cause failure. The procedure does not allow the user to divert from the prescribed sequence of observations. Creating the flow charts is a time-consuming process requiring advance decision expertise. Once created, the flowcharts are difficult to modify and maintain, with the result that eliminating or adding observations or failures to the flowchart often requires a complete redesign. Therefore, in practice, the flowchart-based decision tools rapidly become out-of-date.
Graphical probabilistic models are decision models of decisions and observations. Expert knowledge or decision data is used to create the graphical probabilistic model. The model captures causal dependencies between the decisions and the observations. They can produce dynamic decision procedures, when used with algorithmic engines. At each step of the decision sequence, the user has full flexibility in choosing the next observation to perform.
Decision flowcharts for diagnostic applications diagnose only single-defect failures. Their design assumes that one and only one component failed; and therefore, a single deterministic sequence of tests will lead to the proper conclusion, indicating which component is the root-cause of the failure. When the flowchart is used in diagnosis, the user has to adhere strictly to the sequence of observations prescribed in it. The flowchart has to be abandoned as a decision tool if, at some point at the observation sequence, the user is not able to perform the specified observation.
Graphical probabilistic models are a much more powerful form of expressing decision knowledge. They are much more flexible and useful than flowcharts and are much easier to modify. One can explicitly express effectiveness and cost of observation information in them. During diagnosis, an algorithmic engine uses a probabilistic model to produce ranked failures and ranked observations each time a new observation is made. The engine dynamically generates a sequence of observations, which are optimized for convergence to root-cause and for cost. The user has full flexibility in choosing which of the ranked observations to perform at each stage of the diagnosis.
Currently, there exists a need for a tool that automatically converts an existing flowchart into a graphical probabilistic model. Such a tool is particularly desirable in order to create powerful graphical probabilistic models in order to produce better decision procedures. A further advantage of such a conversion tool is that it would enable a user to take advantage of the flexibility of use and ease of modification that is possible with graphical models and impossible with conventional decision flowcharts.